← Search

Paarth Neekhara

4 accepted papers

2025

Koel-TTS: Enhancing LLM based Speech Generation with Preference Alignment and Classifier Free Guidance

EMNLP 2025

Autoregressive speech token generation models produce speech with remarkable variety and naturalness but often suffer from hallucinations and undesired vocalizations that do not conform to conditioning inputs. To address these challenges, we introduce Koel-TTS, an encoder-decoder transformer model f

2025

Low Frame-rate Speech Codec: a Codec Designed for Fast High-quality Speech LLM Training and Inference

ICASSP 2025accepted

Large language models (LLMs) have significantly advanced audio processing through audio codecs that convert audio into discrete tokens, enabling the application of language modeling techniques to audio data. However, audio codecs often operate at high frame rates, resulting in slow training and infe…

Cited by 0SourceScholar
2024

SelfVC: Voice Conversion With Iterative Refinement using Self Transformations

ICML 2024poster

We propose SelfVC, a training strategy to iteratively improve a voice conversion model with self-synthesized examples. Previous efforts on voice conversion focus on factorizing speech into explicitly disentangled representations that separately encode speaker characteristics and linguistic content.…

Cited by 7SourcePDFScholar
2023

ACE-VC: Adaptive and Controllable Voice Conversion Using Explicitly Disentangled Self-Supervised Speech Representations

ICASSP 2023accepted

In this work, we propose a zero-shot voice conversion method using speech representations trained with self-supervised learning. First, we develop a multi-task model to decompose a speech utterance into features such as linguistic content, speaker characteristics, and speaking style. To disentangle…

Cited by 0SourceScholar